Electronic apparatus and method of controlling thereof

ABSTRACT

A robot vacuum cleaner is provided. The robot vacuum cleaner includes a camera, a memory configured to store an artificial intelligence model trained to identify an image from an input image and shape information corresponding to each of a plurality of objects, and a processor configured to control the electronic apparatus by being connected to the camera and the memory, wherein the processor is configured to input an image obtained by the camera to the artificial intelligence model to identify an object included in the image, obtain shape information corresponding to the identified object among the plurality of shape information stored in the memory, and set a traveling path of the robot vacuum cleaner based on the shape information and size information related to the object.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. §119(a) of a Korean patent application number 10-2019-0177124, filed onDec. 27, 2019 in the Korean Intellectual Property Office, the disclosureof which is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to a robot vacuum cleaner and a method ofcontrolling thereof. More particularly, the disclosure relates to arobot vacuum cleaner for setting a traveling path of the robot vacuumcleaner and a method of driving thereof.

2. Description of the Related Art

Robot vacuum cleaners may be apparatuses which automatically clean a narea to be cleaned by sucking foreign materials while driving the areato be cleaned by itself without the user operation.

The robot vacuum cleaner is equipped with various sensors to accuratelyand efficiently detect obstacles scattered in a driving direction. Asensor provided in the robot vacuum cleaner detect positions anddistances of obstacles, and the robot vacuum cleaner determines a movingdirection using a result of sensing.

Research has been conducted only on a method of recognizing varioustypes of obstacles in the home, and research on a specific method ofrecognizing obstacles and using it to set a traveling path of a robotvacuum cleaner is insufficient.

In particular, there is a demand for recognizing an obstacle and settingan optimized traveling path of the robot vacuum cleaner in considerationof a shape of the obstacle.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure is to providea robot vacuum cleaner that identifies an obstacle and set a travelingpath of the robot vacuum cleaner, and a method of controlling thereof.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, a robot vacuum cleaneris provided. The robot vacuum cleaner includes a camera, a memoryconfigured to store an artificial intelligence model trained to identifyan image from an input image and shape information corresponding to eachof a plurality of objects, and a processor configured to control theelectronic apparatus by being connected to the camera and the memory,wherein the processor is configured to input an image obtained by thecamera to the artificial intelligence model to identify an objectincluded in the image, obtain shape information corresponding to theidentified object among the plurality of shape information stored in thememory, and set a traveling path of the robot vacuum cleaner based onthe shape information and size information related to the object.

The memory may be configured to further store size information for eachof the shape information, and wherein the processor is configured toobtain size information corresponding to the shape information based onthe image, or obtain size information corresponding to the shapeinformation based on the size information stored in the memory.

The processor may be configured to identify a plan shape correspondingto the object based on the shape information of the object, and set thetraveling path for avoiding the object based on the plan shape of theobject.

The cleaner may further include an obstacle detecting sensor, whereinthe processor is configured to set the traveling path for avoiding theobject based on sensing data of the obstacle detecting sensor, based onthe plan view corresponding to the object being failed to be obtained.

The memory may be configured to further store information on whetherthere is an object to avoid for each of the plurality of objects,wherein the processor is configured to set the traveling path to climbthe object, based on the object being identified not to avoid based onthe information on whether there is an object to avoid.

The processor may be configured to stop a suction operation of the robotvacuum cleaner while climbing the object.

The memory may be configured to further store first weight valueinformation for each object corresponding to a first area, and secondweight value information for each object corresponding to a second area,wherein the processor is configured to, based on a plurality of objectsbeing identified from the image obtained by the camera, apply the firstweight value information to each of the plurality of objects to obtainfirst area prediction information, apply the second weight valueinformation to each of the plurality of objects to obtain second areaprediction information, and identify an area in which the robot vacuumcleaner is located as any one of the first area or the second area basedon the first area prediction information and the second area predictioninformation.

The cleaner may further include a communication interface, wherein theprocessor is configured to, based on the area in which the robot vacuumcleaner is located being identified any one of the first area or thesecond area, control the communication interface to transmitidentification information on the identified area, a plan view of theidentified area, a plan shape corresponding to each of the plurality ofobjects located in the area to an external server.

The processor may be configured to, based on a user command thatindicates an object being received, identify an object corresponding tothe user command among the objects identified from the image, and drivethe robot vacuum cleaner such that the cleaner moves to a location ofthe identified object based on a plan view with respect to the area inwhich the robot vacuum cleaner is located and a plan shape with respectto at least one object located in the area.

The processor may be configured to obtain shape informationcorresponding to the identified object among the shape informationstored in the memory, and set a traveling path for cleaning surroundingsof the object based on the shape information and size informationrelated to the object.

In accordance with another aspect of the disclosure, a method ofcontrolling a robot vacuum cleaner is provided. The robot vacuum cleanerincludes an artificial intelligence model trained to identify an objectfrom an input image, the method includes inputting an image obtained bya camera to the artificial intelligence model to identify an objectincluded in the image, obtaining shape information corresponding to theidentified object among the plurality of shape information, and settinga traveling path of the robot vacuum cleaner based on the shapeinformation and size information related to the object.

The robot vacuum cleaner may be configured to further include sizeinformation for each of the shape information, and wherein the obtainingthe shape information includes obtaining size information correspondingto the shape information based on the image, or obtaining sizeinformation corresponding to the shape information based on the sizeinformation stored in the memory.

The setting the traveling path may include identifying a plan shapecorresponding to the object based on the shape information of theobject, and setting the traveling path for avoiding the object based onthe plan shape of the object.

The setting the traveling path may include setting the traveling pathfor avoiding the object based on sensing data of an obstacle detectingsensor, based the plan view corresponding to the object being failed tobe obtained.

The robot vacuum cleaner may be configured to further includeinformation on whether there is an object to avoid for each of theplurality of objects, and wherein the setting the traveling pathincludes setting the traveling path to climb the object, based on theobject being identified not to avoid based on the information on whetherthere is an object to avoid.

The method may further include stopping a suction operation of the robotvacuum cleaner while climbing the object.

The robot vacuum cleaner may be configured to further store first weightvalue information for each object corresponding to a first area, andsecond weight value information for each object corresponding to asecond area, and wherein the robot vacuum cleaner further includes,based on a plurality of objects being identified from the image obtainedby the camera, applying the first weight value information to each ofthe plurality of objects to obtain first area prediction information,applying the second weight value information to each of the plurality ofobjects to obtain second area prediction information, and identifying anarea in which the robot vacuum cleaner is located as any one of thefirst area or the second area based on the first area predictioninformation and the second area prediction information.

The method may further include, based on the area in which the robotvacuum cleaner is located being identified any one of the first area orthe second area, transmitting identification information on theidentified area, a plan view of the identified area, a plan shapecorresponding to each of the plurality of objects located in the area toan external server.

The method may further include, based on a user command that indicatesan object being received, identifying an object corresponding to theuser command among the objects identified from the image, and drivingthe robot vacuum cleaner such that the cleaner moves to a location ofthe identified object based on a plan view with respect to the area inwhich the robot vacuum cleaner is located and a plan shape with respectto at least one object located in the area.

The method may further include obtaining shape information correspondingto the identified object among the shape information stored in thememory, and setting a traveling path for cleaning surroundings of theobject based on the shape information and size information related tothe object.

Other aspects, advantages, and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a view illustrating a robot vacuum cleaner according to anembodiment of the disclosure;

FIG. 2 is a block diagram illustrating a configuration of a robot vacuumcleaner according to an embodiment of the disclosure;

FIG. 3 is a view illustrating a traveling path of a robot according toan embodiment of the disclosure;

FIG. 4 is a view illustrating shape information corresponding to anobject according to an embodiment of the disclosure;

FIG. 5 is a view illustrating shape information for each object andinformation on whether there is an object to avoid according to anembodiment of the disclosure;

FIG. 6 is a view illustrating a traveling path of a robot vacuum cleaneraccording to another embodiment of the disclosure;

FIG. 7 is a view illustrating a traveling path of a robot vacuum cleaneraccording to another embodiment of the disclosure;

FIG. 8 is a view illustrating a method of obtaining a plan view of aspace in which a robot vacuum cleaner is located according to anembodiment of the disclosure;

FIG. 9 is a view illustrating a method of acquiring information on aspace in which a robot vacuum cleaner is located according to anembodiment of the disclosure;

FIG. 10 is a view illustrating a robot vacuum cleaner moving to alocation of a specific object according to an embodiment of thedisclosure;

FIG. 11 is a detailed block diagram of a robot vacuum cleaner accordingto an embodiment of the disclosure;

FIG. 12 is a view illustrating a robot vacuum cleaner that communicateswith an external server according to an embodiment of the disclosure;and

FIG. 13 is a flowchart illustrating a method of controlling a robotvacuum cleaner according to an embodiment of the disclosure.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components, and structures.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. It should be apparent to skilled in the art that thefollowing description of various embodiments of the disclosure isprovided for illustration purpose only and not for the purpose oflimiting the disclosure as defined by the appended claims and theirequivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

The terms “have”, “may have”, “include”, and “may include” used in theembodiments of the disclosure indicate the presence of correspondingfeatures (e.g., elements such as numerical values, functions,operations, or parts), and do not preclude the presence of additionalfeatures.

In the description, the term “at least one of A or/and B” is to beunderstood as representing either “A” or “B” or “A and B”.

The expression “1”, “2”, “first”, or “second” as used herein may modifya variety of elements, irrespective of order and/or importance thereof,and only to distinguish one element from another. Accordingly, withoutlimiting the corresponding elements.

When an element (e.g., a first element) is “operatively orcommunicatively coupled with/to” or “connected to” another element(e.g., a second element), an element may be directly coupled withanother element or may be coupled through the other element (e.g., athird element).

Singular forms are intended to include plural forms unless the contextclearly indicates otherwise. The terms “include”, “comprise”, “isconfigured to,” etc., of the description are used to indicate that thereare features, numbers, operations, elements, parts or combinationthereof, and they should not exclude the possibilities of combination oraddition of one or more features, numbers, operations, elements, partsor a combination thereof.

In the disclosure, a ‘module’ or a ‘unit’ performs at least one functionor operation and may be implemented by hardware or software or acombination of the hardware and the software. In addition, a pluralityof ‘modules’ or a plurality of ‘units’ may be integrated into at leastone module and may be at least one processor except for ‘modules’ or‘units’ that should be realized in a specific hardware.

Also, the term “user” may refer to a person who uses an electronicapparatus or an apparatus (e.g., an artificial intelligence (AI)electronic apparatus) that uses the electronic apparatus.

Hereinafter, embodiments will be described in greater detail withreference to the accompanying drawings.

FIG. 1 is a view illustrating a robot vacuum cleaner according to anembodiment of the disclosure.

Referring to FIG. 1 , the robot vacuum cleaner 100 refers to anapparatus driven by electric power and automatically sucks foreignmaterials. In FIG. 1 , it is assumed that the robot vacuum cleaner 100is implemented in a flat shape in close contact with a floor in order tosuck foreign materials on the floor, but this is only an embodiment, andthe robot vacuum cleaner 100 may be implemented in various shapes andsizes.

Referring to FIG. 1 , the robot vacuum cleaner 100 according to anembodiment of the disclosure may include a camera 110 to detect anobject located adjacent to the robot vacuum cleaner 100. For example,the robot vacuum cleaner 100 may obtain a front image of the robotvacuum cleaner 100 through the camera 110 and identify an object locatedin a driving direction of the robot vacuum cleaner 100 based on theobtained image. The object may refer to various objects or situationsthat may interfere with the driving of the robot vacuum cleaner 100 orcause a stop, damage, or failure of the robot vacuum cleaner 100 duringdriving. For example, when the robot vacuum cleaner 100 is driven in ahome, objects may be various such as furniture, electric appliances,rugs, clothes, walls, stairs, thresholds, or the like.

The robot vacuum cleaner 100 according to an embodiment of thedisclosure may set a traveling path or a moving path of the robot vacuumcleaner 100 based on information on the identified object. Theinformation on the identified object may include shape (or form)information of the object and size information related to the object.

The robot vacuum cleaner 100 according to an embodiment may set atraveling path for avoiding a corresponding object while driving of therobot vacuum cleaner 100, a traveling path for climbing thecorresponding object (e.g., climbing over objects), etc. based oninformation on the identified object.

Hereinafter, various embodiments of the disclosure in which the robotvacuum cleaner 100 sets a traveling path will be described.

FIG. 2 is a block diagram illustrating a configuration of a robot vacuumcleaner according to an embodiment of the disclosure.

Referring to FIG. 2 , the robot vacuum cleaner 100 according to anembodiment of the disclosure includes a camera 110, a memory 120, and aprocessor 130.

The camera 110 is a component for acquiring one or more images of thesurroundings of the robot vacuum cleaner 100. The camera 110 may beimplemented as a Red/Green/Blue (RGB) camera, a 3 dimensional (3D)camera, or the like.

Meanwhile, the robot vacuum cleaner 100 according to an embodiment ofthe disclosure may further include a detecting sensor (not shown) inaddition to the camera 110, and the robot vacuum cleaner 100 mayidentify an object based on sensing data of the detecting sensor. Forexample, the detecting sensor may be implemented as an ultrasonicsensor, an infrared sensor, or the like. According to an embodiment,when the detecting sensor is implemented as an ultrasonic sensor, therobot vacuum cleaner 100 may control the ultrasonic sensor to emitultrasonic pulses. Subsequently, when a reflected wave reflected fromthe object is transmitted to the ultrasonic pulses, the robot vacuumcleaner 100 may measure a distance between the object and the robotvacuum cleaner 100 by measuring an elapsed time between them. Inaddition, the ultrasonic sensor may be implemented in various ways,including an ultrasonic proximity sensor. The infrared sensor is adevice that detects infrared light information possessed by an object.The robot vacuum cleaner 100 may identify an object based on infraredlight information obtained through the infrared sensor.

Meanwhile, the disclosure is not limited thereto, and the detectingsensor may be implemented with various types of sensors. The robotvacuum cleaner 100 may analyze a presence or an absence of an object, alocation of the object, a distance to the object, or the like based onsensing data of the detecting sensor, and may set a traveling path ofthe robot vacuum cleaner 100 based on a result of the analysis. Forexample, when it is identified that there is an object in front, therobot vacuum cleaner 100 may rotate the robot vacuum cleaner 100 itselfto the right or left, or move backward.

The memory 120 may store a variety of data such as an O/S softwaremodule that drives the robot vacuum cleaner 100 and applications.

In particular, an artificial intelligence model may be stored in thememory 120. Specifically, the memory 120 according to an embodiment ofthe disclosure may store an artificial intelligence model trained toidentify an object in an input image. The artificial intelligence modelmay be a model trained using a plurality of sample images includingvarious objects. Identifying the object may be understood as acquiringinformation on the object, such as the name and type of the object. Inthis case, the information on the object may be information on theidentified object that is output by the artificial intelligence modelthat identifies the corresponding object.

The artificial intelligence model according to an embodiment is adetermination model trained based on a plurality of images based on anartificial intelligence algorithm, and may be a model based on a neuralnetwork. The trained determination model may be designed to simulate ahuman brain structure on a computer, and may include a plurality ofnetwork nodes having weights that simulate neurons of a human neuralnetwork. The plurality of network nodes may form a connectionrelationship, respectively, so as to simulate a synaptic activity ofneurons that transmit and receive signals through synapses. In addition,the trained judgment model may include, for example, a machine learningmodel, a neural network model, or a deep learning model developed fromthe neural network model. The plurality of network nodes in a deeplearning model may exchange data according to a convolutional connectionrelationship while being located at different depths (or layers).

As an example, the artificial intelligence model may be a convolutionalneural network (CNN) model learned based on an image. The CNN is amultilayer neural network with a special connection structure designedfor voice processing, image processing, or the like. Meanwhile, theartificial intelligence model is not limited to the CNN. For example,the artificial intelligence model may be implemented with at least onedeep neural network (DNN) model among recurrent neural network (RNN),long short term memory network (LSTM), gated recurrent units (GRU), orgenerative adversarial networks (GAN).

The artificial intelligence model stored in the memory 120 may belearned through various learning algorithms such as the robot vacuumcleaner 100 or a separate server/system. The learning algorithm is amethod in which a predetermined target device (e.g., a robot) is trainedusing a plurality of learning data so that the predetermined targetdevice can make a decision or make a prediction by itself. Examples oflearning algorithms are supervised learning, unsupervised learning,semi-supervised learning, or reinforcement learning, and the learningalgorithm in the disclosure is not limited thereto except for beingspecified.

In addition, shape information corresponding to each of a plurality ofobjects may be stored in the memory 120 according to an embodiment ofthe disclosure.

The shape information may include a representative image of thecorresponding object, information on whether the object corresponds to atypical object or an atypical object, and an image viewed from multipleangles of an object.

The image viewed from multiple angles may include an image viewed fromthe front of the object (e.g., front view), an image viewed from theside (e.g., side view), an image viewed from the above of the object(e.g., top view), or the like. However, this is an embodiment and is notlimited thereto. The representative image of an object may refer to anyone of the plurality of images viewed from multiple angles, and mayrefer to any one of a plurality of images obtained by web crawling animage on an object.

Information on whether an object corresponds to a typical object or anatypical object may mean that whether the object corresponds to anobject having a typical shape or a fixed shape (or a constant shape) inwhich a shape of the object is not changed, or an object corresponds toan object in which a shape of the object is not fixed. For example, acup, a bowl, etc. may correspond to the typical object with a fixedshape, and liquid, cable, etc. may correspond to the atypical objectwithout a fixed shape. A detailed description of the shape informationcorresponding to each of the plurality of objects will be additionallydescribed in FIG. 5 .

The processor 130 controls the overall operation of the robot vacuumcleaner 100.

According to an embodiment, the processor 130 may be implemented as adigital signal processor (DSP), a microprocessor, an artificialintelligence (AI) processor, or a timing controller (T-CON) thatprocesses digital image signals. However, it is not limited thereto, andthe processor may include one or more among a central processing unit(CPU), a micro controller unit (MCU), a micro processing unit (MPU), acontroller, and an application processor (AP), a communication processor(CP), or an ARM processor, or may be defined in a corresponding term. Inaddition, the processor 130 may be implemented in a System on Chip (SoC)with a built-in processing algorithm, large scale integration (LSI), orfield programmable gate array (FPGA).

The processor 130 according to an embodiment of the disclosure may inputan image obtained through the camera 110 into an artificial intelligencemodel to identify an object included in an image. The processor 130 mayobtain shape information corresponding to the identified object amongshape information corresponding to each of the plurality of objectsstored in the memory 120. The processor 130 may set a traveling path ofthe robot vacuum cleaner 100 based on the obtained shape information andsize information related to the object. A detailed description of thiswill be made with reference to FIG. 3 .

FIG. 3 is a view illustrating a traveling path of a robot according toan embodiment of the disclosure.

Referring to FIG. 3 , the robot vacuum cleaner 100 according to anembodiment of the disclosure may operate in a home. The camera 110provided in the robot vacuum cleaner 100 may obtain an image byphotographing a front (or a predetermined direction) while the robotvacuum cleaner 100 is traveling. The processor 130 may input theobtained image into the artificial intelligence model to identify anobject included in the image, for example, an object located in front ofthe robot vacuum cleaner 100. As an example, the processor 130 mayidentify a flower pot 10-1 included in an image using the artificialintelligence model. The processor 130 may obtain shape informationcorresponding to the flower pot 10-1 from among the plurality of shapeinformation.

The shape information corresponding to the object may include arepresentative image of the corresponding object, information on whetherthe object corresponds to the typical or atypical object. For example,the shape information corresponding to the flower pot 10-1 may include arepresentative image of the flower pot 10-1, and the flower pot 10-1 mayinclude information that the shape corresponds to the typical object.

The processor 130 may identify size information of the object based onan object image included in the image obtained by the camera 110. Forexample, the processor 130 may identify width and height information ofthe object. Referring to FIG. 3 , the processor 130 may identify widthand height information of the flower pot 10-1 based on an image of theflower pot 10-1 included in the image obtained by the camera 110.

The processor 130 may predict an actual shape of the object based onshape information corresponding to an identified object and sizeinformation related to the object. For example, if the identified objectis a shape object, the processor 130 may identify a plane shapecorresponding to the object. The processor 130 may predict an actualshape and size of the object based on the plane shape of the object andsize information of the object, and may set a traveling path of therobot vacuum cleaner 100 based on the predicted shape and size. Theplane shape of the object may refer to an image vied from the above(e.g., a top view) of a plurality of images viewed from multiple anglesof the object. A detailed description of this will be made withreference to FIG. 4 .

FIG. 4 is a view illustrating shape information corresponding to anobject according to an embodiment of the disclosure.

Referring to FIG. 4 , the processor 130 may identify an object based onan image obtained through the camera 110. For example, the processor 130may identify a bed 10-2 disposed in a bedroom in the home.

The processor 130 may obtain shape information corresponding to theidentified object. Referring to FIG. 4 , the processor 130 may obtainshape information corresponding to the bed 10-2 from among a pluralityof shape information stored in the memory 120. In particular, theprocessor 130 may identify an object, for example, a plane shapecorresponding to the bed 10-2 based on the shape information. The planeshape corresponding to the bed 10-2 may refer to an image viewed fromabove (e.g., a top view) of the bed 10-2 among a plurality of imagesviewed from multiple angles.

The processor 130 according to an embodiment may obtain size informationcorresponding to an object. For example, the processor 130 may obtainsize information corresponding to the identified object based on sizeinformation for each shape information stored in the memory 120. Forexample, the memory 120 may store a plurality of size information foreach shape information corresponding to the bed 10-2. The processor 130may identify at least one of width, height, or length of the bed 10-2based on the image obtained by the camera 110, and obtain sizeinformation of the bed 10-2 based on at least one of the identifiedwidth, height, or length among the plurality of size informationincluded in the shape information of the bed 10-2.

Meanwhile, this is an embodiment, and the processor 130 may obtain sizeinformation of the bed 10-2 such as width, height, and length of the bed10-2 based on the image obtained by the camera 110.

The processor 130 may identify a plane shape based on shape informationcorresponding to an object, and predict a plane shape of an actualobject based on the identified plane shape and size information of theobject. Referring to FIG. 4 , the processor 130 may identify a planeshape (e.g., a square) of the bed 10-2 based on shape informationcorresponding to the bed 10-2, and predict (or obtain) a plane shape (ortop-view) of the bed 10-2 so as to be close to the plane shape of theactual bed 10-2 (for example, the width, height and length of the actualbed 10-2) based on the identified plane shape and size information ofthe bed 10-2.

The processor 130 may set a traveling path for avoiding thecorresponding object based on the plane shape of the object. Forexample, the processor 130 may set traveling path for cleaning anddriving a space by avoiding the bed 10-2 based on the predicted planeshape of the bed 10-2.

According to an embodiment, the robot vacuum cleaner 100 may predict theplane shape of the object to be close to the actual plane shape based onthe image obtained through the camera 110 without having a separatesensor for detecting the object, and set an optimal traveling path basedon the predicted plane shape.

Meanwhile, the robot vacuum cleaner 100 according to an embodiment ofthe disclosure may set a traveling path for climbing (e.g., climbingover the corresponding object) the corresponding object in addition tothe traveling path for avoiding the corresponding object based oninformation on the identified object. A detailed description of thiswill be made with reference to FIG. 5 .

FIG. 5 is a view illustrating shape information for each object andinformation on whether there is an object to avoid according to anembodiment of the disclosure.

Referring to FIG. 5 , the memory 120 according to an embodiment of thedisclosure may store shape information for each object and informationon whether there is an object to avoid.

For example, the memory 120 may store information on the type of object10, a representative image for each object 10, whether contamination iscaused by each object 10, and whether climbing is possible for eachobject 10. FIG. 5 is merely an example of the shape information of theobject, and the shape information of the object may be implemented invarious forms. In addition, the shape information corresponding to eachof a plurality of objects may be received from an external server andstored in the memory 120.

Referring to FIG. 5 , the processor 130 may identify the object 10 basedon the image obtained by the camera 110 and obtain shape informationcorresponding to the identified object 10. For example, if theidentified object 10 is a rug, the processor 130 may obtain shapeinformation corresponding to the rug. The shape informationcorresponding to the rug may include information on a representativeimage of the rug, information on whether the rug corresponds to atypical object or an atypical object, whether the rug is likely to becontaminated, and whether the robot can climb the rug.

The information on whether climbing is possible included in the shapeinformation may include, while the robot vacuum cleaner is traveling,information on whether the corresponding object corresponds to an objectto be avoided, or whether it corresponds to an object to be climbed(e.g., an object that can be climbed over).

Referring to FIG. 5 , since the shape information corresponding to therug indicates that the rug corresponds to an object that can be climbed,if the identified object is a rug, the processor 130 may set a travelingpath to climb the rug, rather than avoiding it.

As another example, if the identified object is a cup, shape informationcorresponding to the cup indicates that the cup does not correspond toan object to be climbed, so the processor 130 may set a traveling pathof the robot vacuum cleaner 100 to avoid the cup.

Meanwhile, if the identified object corresponds to an object that can beclimbed by the object (or corresponds to an object not to be avoided),the processor 130 according to an embodiment of the disclosure maychange a cleaning mode of the robot vacuum cleaner 100 while climbingthe corresponding object.

For example, a general cleaning mode of the robot vacuum cleaner 100 mayperform a sucking operation to suck foreign materials and contaminantson the floor. If an object is sucked into the robot vacuum cleaner 100due to the sucking operation while the robot vacuum cleaner 100 climbsthe identified object, stop of traveling, damage, or failure of therobot vacuum cleaner 100 may be caused. So the processor 130 may changethe cleaning mode of the robot vacuum cleaner 100 based on theidentified object while the robot vacuum cleaner 100 climbs the object.For example, the processor 130 may stop the sucking operation of therobot vacuum cleaner 100 while climbing the identified object. Asanother example, the processor 130 may lower a degree of suction powerof the robot vacuum cleaner 100 while climbing the object.

FIG. 6 is a view illustrating a traveling path of a robot vacuum cleaneraccording to another embodiment of the disclosure.

Referring to FIG. 6 , the robot vacuum cleaner 100 according to anembodiment of the disclosure may further include an obstacle detectingsensor. According to an embodiment, if it is failed to obtain a planeshape corresponding to an object, the processor 130 may set a travelingpath for avoiding the object based on sensing data of the obstacledetecting sensor.

For example, the processor 130 may input an image obtained by the camera110 into an artificial intelligence model to identify an object includedin the image. The processor 130 may obtain shape informationcorresponding to the identified object among a plurality of shapeinformation for each object. The obtained shape information may includeinformation on whether the identified object corresponds to a typicalobject or an atypical object.

According to an embodiment, if the identified object corresponds to theatypical object, the processor 130 may set a traveling path for avoidingthe object based on sensing data of the obstacle detecting sensor.

In other words, since the atypical object refer s to an object in whicha shape of an object is not fixed, if the identified object is anatypical object, the processor 130 may not obtain a plane shape (i.e., atop view) for the identified object. In this case, the processor 130 mayset a traveling path for avoiding the identified object based on thesensing data of the obstacle detecting sensor.

Referring to FIG. 6 , a cable 10-3 is an example of an atypical object.When the cable 10-3 is identified, the processor 130 may set a travelingpath for avoiding the cable 10-3 in consideration of a sensing dataobtained by the obstacle detecting sensor in addition to the imageobtained by the camera 110. However, this is not limited thereto. Forexample, if the identified object is identified to correspond to theatypical object, the processor 130 may obtain at least one among width,height, or length of the corresponding object based on the image topredict a maximum size. The processor 130 may set a traveling path basedon the predicted size of the object.

FIG. 7 is a view illustrating a traveling path of a robot vacuum cleaneraccording to another embodiment of the disclosure.

Referring to FIG. 7 , the processor 130 according to an embodiment ofthe disclosure may identify whether a corresponding object correspondsto an object that is likely to cause contamination based on shapeinformation corresponding to the identified object.

Referring back to FIG. 5 , the memory 120 may store information on thetype of object 10, a representative image for each object 10, whethercontamination is caused for each object 10, and whether climbing ispossible for each object 10.

Whether contamination is caused for each object 10 does not correspondto whether the corresponding object is a target to avoid, but may referto whether there is a possibility that a contamination area may extendwhen the robot vacuum cleaner 100 climbs the corresponding object.

For example, referring to FIG. 7 , since liquid spill 10-4 is not anobject to be avoided, the processor 130 may control the robot vacuumcleaner 100 to climb and travel the liquid spill 10-4. In this case,there is a concern that a range of contamination due to the liquid spill10-4 may extend in a space due to a driver (e.g., a wheel, etc.) locatedat the bottom of the robot vacuum cleaner 100, a suction unit, or thelike. As another example, excrement of a pet is not an object to beavoided, but may be an object that is concerned about causingcontamination.

When an object is identified as a contamination-causing object based onshape information of the object, the processor 130 may set a travelingpath for avoiding the object.

FIG. 8 is a view illustrating a method of obtaining a plan view of aspace in which a robot vacuum cleaner is located according to anembodiment of the disclosure.

Referring to FIG. 8 , the robot vacuum cleaner 100 may photographvarious images while traveling areas on a map, and input thephotographed images to a plurality of artificial intelligence models torecognize objects located within the area.

In addition, the robot vacuum cleaner 100 may divide the space into aplurality of areas. For example, the robot vacuum cleaner 100 mayidentify a point where there is a dividing line or threshold on thefloor, a point where a movable width is narrowed, a point where there isa wall, a point where the wall starts, a point where the wall ends, apoint where there is a door, or the like based on the image obtained bythe camera 110. The processor 130 may divide the space (e.g., a home)into a plurality of areas (e.g., a living room, a bedroom, a bathroom, akitchen, etc.) by using the identified point as a boundary between theareas. Hereinafter, for convenience of description, it is assumed thatan area refers to a lower concept and a space refers to an upperconcept, that is, a set of areas.

Meanwhile, the processor 130 according to an embodiment of thedisclosure may use information on an object located within an area inorder to obtain area information corresponding to each of a plurality ofareas. The area information may refer to information for identifyingeach of the plurality of areas. The area information may be composed ofan identification name, an identification number, etc. indicating eachof the plurality of areas. In addition, the area information may includeinformation on the use of each of the plurality of areas. For example,the plurality of areas may be defined as a living room, a bathroom, abedroom, or the like by the area information. In addition, informationon the object may include name, type, etc. of an object.

A detailed description will be described with reference to FIG. 9 .

FIG. 9 is a view illustrating a method of acquiring information on aspace in which a robot vacuum cleaner is located according to anembodiment of the disclosure.

Referring to FIG. 9 , the processor 130 according to an embodiment ofthe disclosure may obtain area information corresponding to acorresponding area based on an object identified within the area. Thearea information may include information on a purpose of the area, thename of the area, or the like.

For example, when only a bookshelf is identified in a first area, theprocessor 130 may identify the first area as a study room. As anotherexample, when a bed and a bookcase are identified in a second area, theprocessor 130 may identify the second area as a bedroom. However, theseare only examples. Meanwhile, according to another example, when only atelevision (TV) is identified in a third area, the third area may be astudy room or a living room, and thus obtaining area information usingonly a table as shown in FIG. 9 is somewhat less reliable or unclear.

Accordingly, the processor 130 according to an embodiment of thedisclosure may obtain prediction information corresponding to acorresponding area by using weight information for each area.

The memory 120 according to an embodiment may store first weightinformation for each object corresponding to the first area and secondweight information for each object corresponding to the second area. Theweight information may be defined as the Table 1 below.

TABLE 1 0 1 2 3 4 5 Estimated area Living room Study room Room KitchenBedroom bathroom 0 Air conditioner 1 0.8 0.8 0.5 0.8 0.1 1 Refrigerator0.2 0.1 0.1 1 0.1 0.1 2 TV 1 0.5 0.8 0.7 0.9 0.1 3 Bed 0.2 0.2 0.5 0.1 10.1 4 Sofa 1 0.8 0.8 0.1 0.4 0.1 5 Bookshelf 0.8 1 0.7 0.1 0.3 0.1 6Washing 0.2 0.1 0.2 0.3 0.2 0.8 machine

The processor 130 according to an embodiment of the disclosure mayobtain area prediction information for each of a plurality of areas byusing Table 1 and Equation 1 below to an object identified in areas.

$\begin{matrix}{{{Area}(j)} = {{\sum}_{i = 0}^{m}\left\{ \begin{matrix}{{{k\_ obj}\lbrack i\rbrack}\lbrack j\rbrack} & {\exists{{{Obj}\lbrack i\rbrack} \in {AREA}}} \\0 & {{otherwise}.}\end{matrix} \right.}} & {{Equation}1}\end{matrix}$ Find_Area(j) = MAX(Area(j)), 0 ≤ j ≤ n

As an example, when a plurality of objects are identified in an imageobtained by a camera, that is, when a plurality of objects areidentified within a specific area, the processor 130 may apply firstweight information to each of the plurality of objects, and obtain firstspace prediction information. The processor 130 may apply second weightinformation to each of the plurality of objects, and obtain second spaceprediction information.

For example, it may assume that a TV and a sofa are identified in aspecific area. In this case, the processor 130 may obtain areaprediction information corresponding to each of the plurality of areasas shown in Table 2 below based on Table 1 and Equation 1.

TABLE 2 0 1 2 3 4 5 Estimated area Living room Study room Room KitchenBedroom bathroom 0 Air conditioner 0 0 0 0 0 0 1 Refrigerator 0 0 0 0 00 2 TV One 0.5 0.8 0.7 0.9 0.1 3 Bed 0 0 0 0 0 0 4 Sofa One 0.8 0.8 0.10.4 0.1 5 Bookshelf 0 0 0 0 0 0 6 Washing 0 0 0 0 0 0 machine Sum 1.31.6 0.8 1.3 0.2

Since TVs and sofas are often located in a living room area relative toother areas, the first weight information corresponding to the livingroom may give a high weight to the TV and sofa, and a small weight to awashing machine.

The processor 130 may identify an area in which the robot vacuum cleaner100 is located as either the first area or the second area based on thefirst area prediction information and the second area predictioninformation.

Referring to Table 2, when a TV and a sofa are identified in a specificarea, the processor 130 may obtain 2 as area prediction information in aliving room area and 0.2 as area prediction information in a bathroomarea. The processor 130 may identify the specific area as a living roomarea.

FIG. 10 is a view illustrating a robot vacuum cleaner moving to alocation of a specific object according to an embodiment of thedisclosure.

Referring to FIG. 10 , the processor 130 according to an embodiment ofthe disclosure may assign area information to each of a plurality ofareas included in a space. For example, a first area where a TV and asofa are identified may be identified as a living room, and a secondarea where a basin is identified may be identified as a bathroom. Asanother example, a third area where a dressing table and a bed areidentified may be identified as a bedroom.

A plan view of an area and a plan view of a plurality of objectsillustrated in FIG. 10 may be referred to as map information of thespace. The robot vacuum cleaner 100 according to an embodiment maytransmit map information of the space to a server or transmit it to anexternal device (e.g., a user terminal device) to provide it to a user.

Meanwhile, the robot vacuum cleaner 100 according to an embodiment ofthe disclosure may receive a user command. As an example, the usercommand may be a command indicating a specific object. The user commandmay be a voice command, a text command, or a control command receivedfrom a remote control device or an external device. As another example,a user terminal device may display map information of the space, and therobot vacuum cleaner 100 may receive the user command indicating aspecific object through the user terminal device.

For example, when the user command indicating a specific object (e.g.,“clean the surroundings of the TV”) is received, the processor 130 mayidentify a location of the object corresponding to the user commandbased on the map information of the space. For example, the processor130 may identify a TV 10-5 included in “clean the surroundings of theTV” by performing voice recognition on the user command. The processor130 may obtain location information of the TV 10-5 in the space based ona plan view of the area and a plan shape of at least one object locatedin the area. The processor 130 may move the robot vacuum cleaner 100 tothe TV 10-5 based on the obtained location information of the TV 10-5.

Specifically, the processor 130 may control the robot vacuum cleaner 100to change the surroundings of the TV 10-5 according to a user command.

Meanwhile, the processor 130 according to an embodiment of thedisclosure may move the robot vacuum cleaner 100 to a location of anobject corresponding to a user command and perform a cleaning operationbased on shape information of the object. For example, the processor 130may set an optimal traveling path for avoiding the corresponding objectbased on the shape information of the object corresponding to the usercommand, and move the robot vacuum cleaner 100 to clean the surroundingsof the corresponding object without collision with the correspondingobject based on the set traveling path (i.e., by avoiding the object).

For example, the processor 130 may obtain shape informationcorresponding to the TV 10-5 from shape information for each of theplurality of the objects according to the user command, and obtain atraveling path for avoiding the TV 10-5 based on the shape informationcorresponding to the TV 10-5. The processor 130 may control the robotvacuum cleaner 100 to avoid the TV 10-5 and efficiently clean thesurroundings of the TV 10-5.

FIG. 11 is a detailed block diagram of a robot vacuum cleaner accordingto an embodiment of the disclosure.

Referring to FIG. 11 , the robot vacuum cleaner 100 according to anembodiment of the disclosure may include a camera 110, a memory 120, aprocessor 130, a display 140, a communication interface 150, and a userinterface 160.

The camera 110 may be implemented as an RGB camera, a 3D camera, or thelike. The 3D camera may be implemented as a TOF camera including a timeof flight (TOF) sensor and an infrared light. The 3D camera may includean infrared (IR) stereo sensor. The camera sensor may be one that uses acharge-coupled device (CCD), a complementary metal-oxide-semiconductor(CMOS), or the like, but is not limited thereto. When the camera 110includes a CCD, the CCD may be implemented as a Red/Green/Blue (RGB)CCD, an infrared (IR) CCD, or the like.

The memory 120 may store an artificial intelligence model learned toidentify an object in an input image.

Meanwhile, the memory 120 may include ROM, RAM (ex. dynamic RAM (DRAM),synchronous DRAM (SDRAM), Double data rate SDRAM (DDR SDRAM)), or thelike, and may be implemented together with the processor 130.

Functions related to artificial intelligence according to the disclosureare operated through the processor 130 and the memory 120. The processor130 may be composed of one or a plurality of processors. In this case,one or more processors may be a general-purpose processor such as a CPU,AP, digital signal processor (DSP), etc., or a graphics-only processorsuch as a GPU, a vision processing unit (VPU), or an artificialintelligence-only processor such as an NPU. One or more processorscontrol to process input data according to a predefined operation ruleor an artificial intelligence model stored in the memory 120.Alternatively, when one or more processors are the artificialintelligence-only processors, the artificial intelligence-only processormay be designed with a hardware structure specialized for processing aspecific artificial intelligence model.

A predefined motion rule or an artificial intelligence model ischaracterized by being generated through learning. The generatingthrough learning means that a basic artificial intelligence model islearned using a plurality of learning data by a learning algorithm, sothat the predefined motion rule or an artificial intelligence model setto perform a desired characteristic (or purpose) is generated. Suchlearning may be performed in the device itself on which the artificialintelligence according to the disclosure is performed, or may beperformed through a separate server and/or system. Examples of thelearning algorithm include supervised learning, unsupervised learning,semi-supervised learning, or reinforcement learning, but are not limitedto the examples described above.

The artificial intelligence model may be composed of a plurality ofneural network layers. Each of the plurality of neural network layershas a plurality of weight values, and performs a neural networkoperation through an operation between an operation result of a previouslayer and a plurality of weight values. The plurality of weight valuesof the plurality of neural network layers may be optimized by thelearning result of the artificial intelligence model. For example, aplurality of weight values may be updated to reduce or minimize a lossvalue or a cost value obtained from the artificial intelligence modelduring the learning process. The artificial neural network may include adeep neural network (DNN), for example, convolutional neural network(CNN), deep neural network (DNN), recurrent neural network (RNN),restricted Boltzmann machine (RBM), deep belief network (DBN),bidirectional recurrent deep neural network (BRDNN), or deep Q-Networks,or the like, but is not limited thereto.

The display 140 may be implemented as a display including aself-luminous element or a display including a non-luminescent elementand a backlight. For example, the display may be implemented in varioustypes of displays such as Liquid Crystal Display (LCD), Organic LightEmitting Diodes (OLED) display, Light Emitting Diodes (LED), micro LED,Mini LED, Plasma Display Panel (PDP), Quantum dot (QD) display, Quantumdot light-emitting diodes (QLED), or the like. The display 140 mayinclude a driving circuit, a backlight unit, or the like which may beimplemented in forms such as an a-si TFT, a low temperature poly silicon(LTPS) TFT, an organic TFT (OTFT), or the like. Meanwhile, the display140 may be implemented as a touch screen combined with a touch sensor, aflexible display, a rollable display, a 3D display, a display in which aplurality of display modules are physically connected, or the like. Theprocessor 130 may control the display 140 to output status informationof the robot vacuum cleaner 100 obtained according to the variousembodiments described above. The status information may include variousinformation related to driving of the robot vacuum cleaner 100, such asa cleaning mode of the robot vacuum cleaner 100, information related toa battery, information on whether to return to a docking station 200, orthe like.

The communication interface 150 is a component for the robot vacuumcleaner 100 to communicate with at least one external device to exchangesignals/data. For this, the communication interface 150 may include acircuit.

The communication interface 150 may include a wireless communicationmodule, a wired communication module, or the like.

The wireless communication module may include at least one of a Wi-Ficommunication module, a Bluetooth module, an infrared data association(IrDA) module, a third generation (3G) mobile communication module, afourth generation (4G) mobile communication module, a 4G long termevolution (LTE) communication module.

The wired communication module may be implemented as a wired port suchas a Thunderbolt port, a USB port, or the like.

The user interface 160 may include one or more buttons, a keyboard, amouse, or the like. In addition, the user interface 160 may include atouch panel implemented together with a display (not shown) or aseparate touch pad (not shown).

The user interface 160 may include a microphone to receive a user'scommand or information by voice, or may be implemented together with thecamera 110 to recognize the user's command or information in a motionform.

FIG. 12 is a view illustrating a robot vacuum cleaner that communicateswith an external server according to an embodiment of the disclosure.

Referring to FIG. 12 , the robot vacuum cleaner 100 may communicate withexternal devices 300-1 and 300-2, which may be smartphones, and a serverdevice 500. In this case, the robot vacuum cleaner 100 may communicatewith the external devices 300-1, 300-2 and 500 through a relay device400 configured with a router or the like.

For example, when an area in which the robot vacuum cleaner 100 islocated is identified as either a first area or a second area, theprocessor 130 may control the communication interface 150 to provideidentification information (e.g., area information) on the identifiedarea, a plan view of the identified area, and a plane shapecorresponding to each of the plurality of objects located in the area toan external server or the external devices 300-1 and 300-2.

In addition, the robot vacuum cleaner 100 may move to any one of aplurality of areas included in a space in which the robot vacuum cleaner100 is located according to a control signal received from the externaldevice 300-1, which may be a smartphone, or the external device 300-2,or move to any one of the plurality of objects located in the space.

FIG. 13 is a flowchart illustrating a method of controlling a robotvacuum cleaner according to an embodiment of the disclosure.

Referring to FIG. 13 , a method of controlling a robot vacuum cleanerincluding an artificial intelligence model trained to identify an objectin an input image and shape information corresponding to each of aplurality of objects first inputs an image obtained by a camera to theartificial intelligence model and identify an object included in theimage in operation S1310.

Shape information corresponding to the identified object is obtainedfrom among the plurality of shape information in operation S1320.

A traveling path of the robot vacuum cleaner is set based on the shapeinformation and size information related to the object in operationS1330.

The robot vacuum cleaner may further include size information for eachshape information. The operation S1320 that obtains shape information,according to an embodiment, may include obtaining size informationcorresponding to shape information based on the image, or obtaining sizeinformation corresponding to shape information based on the sizeinformation stored in a memory.

The operation S1330 that sets a traveling path includes identifying aplane shape corresponding to an object based on the shape information ofthe object, and setting a traveling path for avoiding the object basedon a plane shape of the object.

In addition, the operation S1330 of setting the traveling path mayinclude setting a traveling path for avoiding the object based onsensing data of an obstacle detecting sensor, if it is fail to obtain aplane shape corresponding to the object.

In addition, the robot vacuum cleaner may further include information onwhether there is an object to avoid by a plurality of objects, and theoperation S1330 of setting a traveling path according to an embodimentmay include setting a traveling path to climb an object, if it isidentified that the object is not a target to avoid based on theinformation on whether there is an object to avoid.

The method of controlling according to an embodiment may further includestopping a suction operation of the robot vacuum cleaner while climbingthe object.

In addition, the robot vacuum cleaner may further store first weightinformation for each object corresponding to the first area and secondweight information for each object corresponding to the second area, andthe method of controlling according to an embodiment may further includeobtaining first area prediction information by applying first weightvalue information to each of the plurality of objects, obtaining secondarea prediction information by applying second weight information toeach of the plurality of objects, and identifying an area in which therobot vacuum cleaner is located as either the first area or the secondarea based on the first area prediction information and the second areaprediction information.

When the area in which the robot vacuum cleaner is located is identifiedas either the first area or the second area, the method of controlling,according to an embodiment, may include transmitting identificationinformation on the identified area, a plan view for the identified area,and a plan shape corresponding to each of the plurality of objectslocated in the area to the external server.

In addition, when a user command that indicates an object is received,the method of controlling, according to an embodiment, may includeidentifying an object corresponding to the user command among objectsidentified in the image, and driving the robot vacuum cleaner so thatthe robot vacuum cleaner moves to a location of the identified objectbased on a plan view of the area where the robot vacuum cleaner islocated, and a plane shape of at least one object.

The method of controlling, according to an embodiment, may furtherinclude obtaining shape information corresponding to the identifiedobject among a plurality of shape information, and setting a travelingpath for cleaning the surroundings of the object based on the shapeinformation and size information related to the object.

However, the various embodiments of the disclosure may be applied notonly to a robot vacuum cleaner but also to all movable electronicdevices.

Various embodiments described above may be embodied in a recordingmedium that may be read by a computer or a similar apparatus to thecomputer by using software, hardware, or a combination thereof. In somecases, the embodiments described herein may be implemented by theprocessor itself. In a software configuration, various embodimentsdescribed in the specification such as a procedure and a function may beembodied as separate software modules. The software modules mayrespectively perform one or more functions and operations described inthe specification.

Meanwhile, computer instructions for performing the processing operationof the robot vacuum cleaner according to various embodiments of thedisclosure described above may be stored in a non-transitorycomputer-readable medium. When a computer instruction stored in thenon-transitory computer-readable medium is executed by a processor of aspecific device, it allows a specific device to perform the processingoperation in the robot vacuum cleaner 100 according to theabove-described various embodiments.

The non-transitory computer readable recording medium refers to a mediumthat stores data and that can be read by devices. For example, thenon-transitory computer-readable medium may be CD, DVD, a hard disc,Blu-ray disc, USB, a memory card, ROM, or the like.

While the disclosure has been shown described with reference to variousembodiments thereof, it will be understood by those skilled in the artthat various changes in form and details may be made therein withoutdeparting from the spirit and scope of the disclosure as defined by theappended claims and their equivalents.

What is claimed is:
 1. An electronic apparatus comprising: a camera; amemory configured to store an artificial intelligence model trained toidentify an image from an input image, shape information correspondingto each of a plurality of objects, and information on an object to beavoided, first weight value information for each object corresponding toa first area, and second weight value information for each objectcorresponding to a second area; and a processor configured to controlthe electronic apparatus by being connected to the camera and thememory, wherein the processor is further configured to: input an imageobtained by the camera to the artificial intelligence model to identifyan object included in the image, obtain shape information correspondingto the identified object among the shape information stored in thememory, based on the identified object corresponding to the object to beavoided based on the information, identify whether the identified objectis an object causing contamination based on the shape information, basedon the identified object corresponding to the object to be avoided orthe identified object corresponding to an object causing contamination,set a traveling path of the electronic apparatus based on the shapeinformation and size information related to the identified object, andbased on the identified object not corresponding to the object to beavoided or the identified object not corresponding to the object causingcontamination, set the traveling path to climb the identified object,and wherein the processor is further configured to: based on a pluralityof objects being identified from the image obtained by the camera, applythe first weight value information to each of the plurality of objectsto obtain first area prediction information, apply the second weightvalue information to each of the plurality of objects to obtain secondarea prediction information, and identify an area in which theelectronic apparatus is located as any one of the first area or thesecond area based on the first area prediction information and thesecond area prediction information.
 2. The electronic apparatus of claim1, wherein the memory is further configured to store size informationfor each of the shape information, and wherein the processor is furtherconfigured to obtain size information corresponding to the shapeinformation based on the image, or obtain size information correspondingto the shape information based on the size information stored in thememory.
 3. The electronic apparatus of claim 1, wherein the processor isfurther configured to: identify a plan shape corresponding to theidentified object based on the shape information of the identifiedobject, and set the traveling path for avoiding the identified objectbased on the plan shape of the identified object.
 4. The electronicapparatus of claim 1, further comprising: an obstacle detecting sensor,wherein the processor is further configured to set the traveling pathfor avoiding the identified object based on sensing data of the obstacledetecting sensor, based on a plan view corresponding to the identifiedobject being failed to be obtained.
 5. The electronic apparatus of claim1, wherein the processor is further configured to stop a suctionoperation of the electronic apparatus while climbing the identifiedobject.
 6. The electronic apparatus of claim 1, further comprising: acommunication circuitry, wherein the processor is further configured to,based on the area in which the electronic apparatus is located beingidentified as any one of the first area or the second area, control thecommunication circuitry to transmit identification information on theidentified area, a plan view of the identified area, and a plan shapecorresponding to each of the plurality of objects located in the area toan external server.
 7. The electronic apparatus of claim 1, wherein theprocessor is further configured to: based on receiving a user commandthat indicates an object, identify an object corresponding to the usercommand among the objects identified from the image, and drive theelectronic apparatus such that the electronic apparatus moves to alocation of the identified object based on a plan view with respect toan area in which the electronic apparatus is located and a plan shapewith respect to at least one object located in the area.
 8. Theelectronic apparatus of claim 7, wherein the processor is furtherconfigured to: obtain shape information corresponding to the identifiedobject among the shape information stored in the memory, and set atraveling path for cleaning surroundings of the identified object basedon the shape information and size information related to the identifiedobject.
 9. The electronic apparatus of claim 1, wherein the processor isfurther configured to: control the electronic apparatus to perform acleaning operation based on the set traveling path.
 10. A method ofcontrolling an electronic apparatus including an artificial intelligencemodel trained to identify an object from an input image, the methodcomprising: inputting an image obtained by a camera to the artificialintelligence model to identify an object included in the image;obtaining shape information corresponding to the identified object amonga plurality of shape information; based on the identified objectcorresponding to an object to be avoided based on information on theobject to be avoided, identifying whether the identified object is anobject causing contamination based on the shape information; based onthe identified object corresponding to the object to be avoided or theidentified object corresponding to an object causing contamination,setting a traveling path of the electronic apparatus based on theobtained shape information and size information related to theidentified object; and based on the identified object not correspondingto the object to be avoided or the identified object not correspondingto the object causing contamination, setting the traveling path to climbthe identified object, wherein the method further comprises: based on aplurality of objects being identified from the image obtained by thecamera, applying first weight value information for each objectcorresponding to a first area, to each of the plurality of objects toobtain first area prediction information; applying second weight valueinformation for each object corresponding to a second area, to each ofthe plurality of objects to obtain second area prediction information;and identifying an area in which the electronic apparatus is located asany one of the first area or the second area based on the first areaprediction information and the second area prediction information. 11.The method of claim 10, further comprising: storing size information foreach shape of the plurality of shape information, wherein the obtainingof the shape information comprises obtaining size informationcorresponding to the shape information based on the image, or obtainingsize information corresponding to the shape information based on thesize information stored in a memory.
 12. The method of claim 10, whereinthe setting of the traveling path comprises: identifying a plan shapecorresponding to the identified object based on the obtained shapeinformation of the object, and setting the traveling path for avoidingthe object based on the plan shape of the object.
 13. The method ofclaim 10, wherein the setting of the traveling path comprises settingthe traveling path to avoid the object based on sensing data of anobstacle detecting sensor, based failing to obtain a plan viewcorresponding to the object.
 14. The method of claim 10, furthercomprising: stopping a suction operation of the electronic apparatuswhile climbing the identified object.
 15. The method of claim 10,further comprising: based on the area in which the electronic apparatusis located being identified as any one of the first area or the secondarea, transmitting, to an external server, identification information onthe identified area, a plan view of the identified area, and a planshape corresponding to each of the plurality of objects located in thearea.
 16. The method of claim 10, further comprising: based on receivinga user command that indicates an object, identifying an objectcorresponding to the user command among the objects identified from theimage; and driving the electronic apparatus such that the electronicapparatus moves to a location of the identified object based on a planview with respect to an area in which the electronic apparatus islocated and a plan shape with respect to at least one object located inthe area.
 17. The method of claim 16, further comprising: obtainingshape information corresponding to the identified object among the shapeinformation stored in a memory; and setting a traveling path forcleaning surroundings of the identified object based on the shapeinformation and size information related to the identified object. 18.The method of claim 10, further comprising: controlling the electronicapparatus to perform a cleaning operation based on the set travelingpath.